Carbon Price Prediction Based on Multi-Factor MEEMD-LSTM Model

A multi-factor carbon price prediction method MEEMD-LSTM is proposed based on traditional Long Short-term Memory (LSTM) neural network. Multi-factor used in carbon price prediction method included the historical carbon price and other factors which affect carbon price fluctuation. The change characteristics of carbon price time series data and other associated factors are extracted in the carbon price prediction. The modified ensemble EMD method (MEEMD) is used to decompose data which is taken as potential input variables into LSTM neural network for prediction and the machine reasoning system based on production rules can automatically search and optimize the parameters of LSTM to further improve the prediction results. The experimental results demonstrate that the proposed method has better prediction effect, robustness and adaptability than the LSTM model without MEEMD decomposition and the single factor MEEMD-LSTM method. Overall it seems that the proposed method is an advanced approach for predicting the non-stationary and non-linear carbon price time series